Data and AI play a crucial role in proving that companies act responsibly and meet their environmental, social and governance (ESG) targets.
Current reality is that ESG data practices are inefficient and inaccurate. ESG data comes from a myriad of sources and is of variable quality. Availability of data is spotty, especially when the scope of data collection and analysis extends beyond company’s own borders to its supply chain and partners. There is plenty of manual work involved and every company does the work by themselves. This results in vast amounts of duplicate work.
Collaborative Data Sharing in the Era of CSRD
European Union’s Corporate Sustainability Reporting Directive (CSRD) came into effect in January this year. It modernises and strengthens the rules concerning the ESG information that companies are required to report. Large stock listed companies are expected to begin reporting in 2025 based on their 2024 data, and other companies will follow suit when CSRD is gradually rolled out. Companies subject to the CSRD will have to report according to European Sustainability Reporting Standards (ESRS), provide the reporting in a standardised digital format, and include their business networks (e.g. supply chains) in their environmental impacts.
Very large number of companies will be affected by growing regulatory demands regarding ESG reporting. What if companies could collaborate more efficiently to meet these needs? Instead of every company collecting the data for themselves there would be clear benefits in forming data sharing practices to make sustainability data available for all parties in the ecosystem. This would help to minimize duplicate work for ecosystem participants, and provide better transparency of the whole value chain for all. In a data ecosystem, sustainability improvements can be driven – and even co-funded – by the whole value chain together.
The Rulebook Approach for Mitigating Risks and Ensuring Fair Data Use in Ecosystems
Despite its clear benefits, data sharing also brings forth several thorny issues regarding business risks, data hygiene, disclosure of trade secrets, corporate security policies, and fair data use. How can a company show that its data and methods can be trusted? How can the ecosystem participants trust each other to not to misuse the data? Do the others get unfair advantage from my data?
Trust-building, fair data use and minimization of risks amongst the ecosystem participants can be tackled by a rulebook approach. Sitra’s fair data economy rulebook model is one leading example of this approach, taking a holistic view to governance of data ecosystems. It helps organizations to form new data sharing networks and implement policies and rules for them.
The rulebook approach also helps data providers and data users to assess any requirements imposed by applicable legislation and contracts appropriately in addition to guiding them in adopting practices that promote the use of data and management of risks. With the aid of the rulebook approach, parties can establish a data network based on mutual trust that shares a common mission, vision, and values. This fosters trust and responsible use of data.
The Imperative of Responsibility and Sustainability in the Industrial Landscape
Responsibility and sustainability have risen as key drivers for creating functioning data ecosystems. This is demonstrated in lighthouse data sharing initiatives, such as Catena-X for the automotive industry. The aim of Catena-X is to grow into a network of more than 200,000 data sharing organizations. Catena-X has picked harmonized and accurate ESG reporting as the most urgent business challenge to be resolved in the ecosystem.
We are headed towards a future where data sharing and collaboration is expected in a massive scale, and potentially influencing everyone who have a stake in the industrial ecosystem. As the importance and impact of these initiatives spread and grow, holistic ESG data governance approach is business critical for building trust in data ecosystems.
Marko Turpeinen, 